Automatic recognition of the modulation scheme embedded in an unknown received signal is an important requirement for civilian, military, and government intelligence bodies when monitoring the radio communication spectrum. Although the subject has been extensively researched for several years and different approaches have been implemented or delineated in theoretical papers, the prior art has traditionally assumed that (a) the carrier frequency of the unknown received signal is given and has zero error, that (b) the input Signal-to-Noise Ratio (SNR) is sufficiently high to classify the modulation correctly, and that (c) the symbol transition of digitally-modulated signals is known. Furthermore, the more recent prior art approaches are limited to off line operation on stored signals. Some of the recent methods employ probabilistic models to minimize misclassification errors, which can achieve good results at SNRs that are as low as 0 dB. As shown in reference [1], however, they assume a priori knowledge of the carrier phase and frequency, the SNR, and the symbol rate of the modulation, and are often limited to digital phase modulation schemes.
Other approaches to automatic classification use statistical pattern recognition techniques, such as Artificial Neural Networks (ANN), to discern discriminating features. As shown in references [2], [3], and [4], ANN classifiers produce reasonably good results under simulated conditions, but their practical behaviour is highly dependent on the training set chosen. Since they can perform learning vector quantization, neural networks are capable of achieving an efficient class definition over a large multi-dimensional feature space. The inherent problem however, is that ANN classifiers have difficulty indentifying the set of meaningful features and to train the network accordingly. Furthermore, the designer does not have much manipulative control over the classification algorithm and may have difficulty applying a priori knowledge of the taxonomy of the modulation schemes. A neural network operates like a black box that requires a new training phase when new features (or signals) to be identified are added. Examples of ANN techniques are described in [4].
Other, more recent, prior art approaches to modulation recognition base their classification decisions on a number of successive serial tests, each yielding a binary output. As described in references [2], [3], and [4], such methods give rise to a decision tree in which the outcome of the first binary decision forces a second binary decision whose outcome determines the next binary decision, etc. This decision tree technique represents an improvement over the vector-based methods described above in that it refines and clarifies, in successive decision levels, the information extracted from the unknown input signal. Its hierarchical structure allocates the computing resources more efficiently. As well, the thresholds established at each decision level may be quickly modified in order to reflect operational changes. These alterations can improve performance accuracy. Notwithstanding these advantages, the decision tree methods published to-date have inherent deficiencies, namely their intolerance to carrier frequency errors, their erratic performance at low SNRs, and their inability to classify modulation schemes reliably under real-time operations.
Other forms of classification techniques are described in the following U.S. patents:
In reference [5], the classifier of an IF signal takes the outputs of the two separate demodulators (one AM, the other FM) to compute different signal statistics (or features) and make six binary decisions based on those statistics. It then classifies the modulating scheme, within a set consisting of CW, DSB, SSB, ASK, FSK, MUX, NOISE, and OTHERS, by using the whole vector of six binary decisions as an input to a logic circuit. The drawback to this method is that it usually performs the computation of the vector features in parallel, without any interaction between these features. It also uses a sub-optimum classification circuit.
In reference [6], the probability distribution of the input signal amplitude is analyzed to estimate the mean, the variance and the amplitude cumulative distribution. This information is combined with the outputs of three phase-locked loops—one tailored to AM signals, one to FM signals and one to DSB signals. The combined information is compared with a number of thresholds to form an information vector which is then compared to a pre-stored series of vectors representing the modulations within the set CW, AM, FM, DSB, SSB, PSK. The main difference between this method and the present invention is the computationally-intensive parallel processing of the feature vector, as opposed to serial processing of the vector which is less computer hungry.
In reference [7], several parameters, including the mean amplitude, the signal-to-noise ratio, and the standard deviation, are computed for each of the frequency lines of the input signal's power spectrum. These parameters are fed in parallel to a neural network for the classification of the input modulation. The main drawback of this method is that it performs the computation of the vector features in parallel over a limited set of features.
In reference [8], the normalized variance of the magnitude of the input baseband signal is computed and compared to a predetermined threshold in order to decide in favour of one of the following modulation types: FSK, FM or QAM. This method is limited by the number of modulation schemes it can identify. As well, it uses only a single feature to perform the identification.
In reference [9], histograms based on the power spectrum of the input signals are computed. Frequency locations and amplitudes are recorded, as well as the location of the centre frequency of the overall spectrum. The particular patterns of each histogram are compared to those of typical modulation schemes, such as AM, FSK, PSK or SSB. The main drawback of this method is that it uses only the spectral representation of the signal to perform its computation.
In reference [10], a method is used to discriminate between an FM signal and a π/4-DQPSK signal in analog AMPS and digital DAMPS systems. The variation in amplitudes of the two different modulation types determines which one is present. The main problem with this method is that it is limited to only two modulation types.
In reference [11], the method uses a neural network to demodulate the signal of a particular digital communication standard. This method differs from the present invention in that it identifies the information content of the signal instead of its format.
In reference [12], a method is used to discriminate between the VSB and QAM signals that are encountered in High Definition TV. The main deficiency with this method is that it is limited to the two modulation schemes it can identify.
In reference [13], the spectral energy distribution of the input signal is compared with the pre-stored energy distributions of FDM/FM signals containing specific parameters. Recognition of a specific form of signal is declared if the input distribution matches one of the pre-stored versions. This method is limited to a single form of signal feature and to a very specific modulation format.
In reference [14], the demodulated signal of an FM receiver is classified according to the voice coding algorithm that processed it. This method differs from the present invention because classification is applied on the demodulated signal.
In reference [15], a method is used to generate a decision-tree classifier from a set of records. It differs from the present invention in that it does not consider the specific classification of modulation formats.
Reference [16] describes a method and apparatus for detecting and classifying signals that are the additive combination of a few constant-amplitude sinusoidal components. The main drawback of this scheme is that it cannot be applied to the modulations treated under the present invention, except for CW.
In reference [17], a sequence of estimated magnitudes is generated from the received signal at the symbol rate, and the result is compared to a predetermined representation of known voiceband digital data modem signals. This method is limited to a single decision level, as opposed to the series of binary decisions performed under the present invention.